| #include <stdint.h> | |
| /* for Reversi (8x8) */ | |
| uint64_t shiftl(uint64_t b){ return (b << 1) & 0xfefefefefefefefe; } | |
| uint64_t shiftr(uint64_t b){ return (b >> 1) & 0x7f7f7f7f7f7f7f7f; } | |
| uint64_t shiftu(uint64_t b){ return (b << 8); } | |
| uint64_t shiftd(uint64_t b){ return (b >> 8); } |
I liked the way Grokking the coding interview organized problems into learnable patterns. However, the course is expensive and the majority of the time the problems are copy-pasted from leetcode. As the explanations on leetcode are usually just as good, the course really boils down to being a glorified curated list of leetcode problems.
So below I made a list of leetcode problems that are as close to grokking problems as possible.
NativeScript is a JavaScript runtime that allows you to write an iOS or Android app entirely in TypeScript or JavaScript with no compromises. That is to say, it provides you with the same level of native access that writing the app in Obj-C/Swift (for iOS) or Java/Kotlin (for Android) does. Not just JSON-serialisable data types like NSString, but all data types are supported. To illustrate what it's all about, here's how you would get the battery level using NativeScript (the below snippet is entirely JS code!):
import { isIOS, isAndroid, Application } from "@nativescript/core";
/**
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
| import SwiftUI | |
| struct ScrollableView<Content: View>: UIViewControllerRepresentable, Equatable { | |
| // MARK: - Coordinator | |
| final class Coordinator: NSObject, UIScrollViewDelegate { | |
| // MARK: - Properties | |
| private let scrollView: UIScrollView | |
| var offset: Binding<CGPoint> |
| // See image comparison https://imgur.com/a/9L2P7GJ | |
| // Read details https://iolite-engine.com/blog_posts/minimal_agx_implementation | |
| // Usage: | |
| // 1. Open "Project Settings" and change "Working Color Space" to "sRGB / Rec709" | |
| // 2. Open `Engine\Shaders\Private\PostProcessTonemap.usf` file | |
| // 3. Find `half3 OutDeviceColor = ColorLookupTable(FinalLinearColor);` line | |
| // 4. Replace it with `half3 OutDeviceColor = ApplyAgX(FinalLinearColor);` line | |
| // 5. Find `half3 ColorLookupTable( half3 LinearColor )` function | |
| // 6. After the scope of the function, add the code below and run `RecompileShaders Changed` from console |